Experiences of discrimination across the life course among pregnancy planners in the United States and Canada
Bibliographic record
Abstract
Little is known about discrimination among pregnancy planners. We used questionnaire data from Pregnancy Study Online (PRESTO), a preconception cohort study, to characterize experiences, attributions, and responses to discrimination (n = 10,460). Eligible participants were assigned female at birth, aged 21–45 years, U.S. or Canadian residents, and not using contraception or fertility treatment. Participants completed a supplemental questionnaire (2013–2024) that included the Philadelphia Urban ACE Survey, Williams' Everyday Discrimination and Major Experiences of Discrimination scales, and Krieger's instrument on responses to discrimination. Mean age at enrollment was 30.9 years. Overall, 83.8 % of participants identified as non-Hispanic White, and 50.4 % had ≥17 years education. Discrimination across the life course varied: 11 % of participants reported childhood racial discrimination, 80.3 % reported ever experiencing everyday discrimination, and 47.2 % reported ever experiencing lifetime discrimination. The most prevalent types of everyday discrimination included being perceived as not smart (63.4 %) and being treated with disrespect (62.6 %), while job-related discrimination was the most frequently-reported lifetime experience (33.9 %). Most Black participants (non-Hispanic and Hispanic) reported their race or ethnicity as one of the main reasons they were discriminated against (87.7 % and 80 %, respectively), while sex or gender was most commonly-reported by other racial and ethnic groups (range: 75.9–82.4 %). Most participants responded passively to discrimination: keeping it to themselves and accepting it as a fact of life (37.4 %). All participants other than non-Hispanic White reported greater exposure to discrimination across the life course, and attributions for discrimination ( e.g., race, gender, education, income level) varied across racial and ethnic groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".